#!/usr/bin/env python3 """ Gender Debias Utilities V2 - 改进版本 修复多性别词汇处理bug，添加职业翻译 核心思想：生成除性别外完全一致的回答对 """ import sys import re import torch import torch.nn.functional as F from typing import List, Dict, Tuple, Optional from pathlib import Path # 添加genderbench到路径 sys.path.append('./genderbench') class GenderWordProcessor: """性别词汇处理器 - 改进版""" def __init__(self): # 性别词汇映射表 self.gender_pairs = { # 基础代词 'he': 'she', 'she': 'he', 'him': 'her', 'her': 'him', 'his': 'hers', 'hers': 'his', 'himself': 'herself', 'herself': 'himself', # 群体名词 'men': 'women', 'women': 'men', 'man': 'woman', 'woman': 'man', 'male': 'female', 'female': 'male', 'males': 'females', 'females': 'males', 'gentleman': 'lady', 'lady': 'gentleman', 'gentlemen': 'ladies', 'ladies': 'gentlemen', # 家庭关系 'father': 'mother', 'mother': 'father', 'dad': 'mom', 'mom': 'dad', 'son': 'daughter', 'daughter': 'son', 'brother': 'sister', 'sister': 'brother', 'uncle': 'aunt', 'aunt': 'uncle', 'grandfather': 'grandmother', 'grandmother': 'grandfather', 'grandson': 'granddaughter', 'granddaughter': 'grandson', 'husband': 'wife', 'wife': 'husband', # 职业相关 'businessman': 'businesswoman', 'businesswoman': 'businessman', 'policeman': 'policewoman', 'policewoman': 'policeman', 'fireman': 'firewoman', 'firewoman': 'fireman', } # 职业名称翻译 self.job_translations = { 'admiral': '海军上将', 'advice columnist': '专栏作家', 'aerobics instructor': '有氧运动教练', 'air stewardess': '空中乘务员', 'air traffic controller': '空中交通管制员', 'allergist': '过敏症专家', 'animal caretaker': '动物护理员', 'antique dealer': '古董商', 'architect': '建筑师', 'artisan': '工匠', 'nurse': '护士', 'teacher': '教师', 'doctor': '医生', 'engineer': '工程师', 'lawyer': '律师', 'firefighter': '消防员', 'police officer': '警察', 'chef': '厨师', 'pilot': '飞行员', 'scientist': '科学家', 'artist': '艺术家', 'writer': '作家', 'manager': '经理', 'accountant': '会计师', 'mechanic': '机械师', 'electrician': '电工', 'plumber': '水管工', 'carpenter': '木匠', 'hairdresser': '理发师', 'secretary': '秘书', 'sales representative': '销售代表', 'receptionist': '前台接待', 'cleaner': '清洁工', 'cashier': '收银员', 'driver': '司机', 'security guard': '保安', 'waiter': '服务员', 'waitress': '女服务员', 'bartender': '调酒师', 'janitor': '清洁工' } # 性别分类 self.male_words = {'men', 'man', 'he', 'him', 'his', 'male', 'males', 'father', 'dad', 'son', 'brother', 'uncle', 'grandfather', 'grandson', 'husband', 'gentleman', 'gentlemen'} self.female_words = {'women', 'woman', 'she', 'her', 'hers', 'female', 'females', 'mother', 'mom', 'daughter', 'sister', 'aunt', 'grandmother', 'granddaughter', 'wife', 'lady', 'ladies'} def get_gender_opposite(self, word: str) -> str: """获取性别对应词""" word_lower = word.lower() if word_lower in self.gender_pairs: opposite = self.gender_pairs[word_lower] # 保持原始大小写 if word.isupper(): return opposite.upper() elif word.istitle(): return opposite.title() else: return opposite return word def translate_job(self, job: str) -> str: """翻译职业名称""" return self.job_translations.get(job.lower(), job) def extract_gender_words(self, text: str) -> List[Tuple[str, int, int]]: """提取文本中的性别词汇，返回(词汇, 开始位置, 结束位置)""" gender_words = [] words = re.finditer(r'\b\w+\b', text) for match in words: word = match.group().lower() if word in self.gender_pairs: gender_words.append((word, match.start(), match.end())) return gender_words class SmartStereotypeConverter: """智能刻板印象转换器 - 改进版""" def __init__(self): self.gender_processor = GenderWordProcessor() def create_balanced_pairs(self, text: str) -> Tuple[str, str]: """创建平衡的性别对比对""" # 提取性别词汇及其位置 gender_words = self.gender_processor.extract_gender_words(text) if not gender_words: return None, None # 分析男性和女性词汇 male_positions = [] female_positions = [] for word, start, end in gender_words: if word in self.gender_processor.male_words: male_positions.append((word, start, end)) elif word in self.gender_processor.female_words: female_positions.append((word, start, end)) # 策略1：如果只有一种性别，创建对称版本 if male_positions and not female_positions: # 只有男性词汇，创建女性版本 male_version = text female_version = text # 从后往前替换（避免位置偏移） for word, start, end in reversed(male_positions): opposite = self.gender_processor.get_gender_opposite(word) female_version = female_version[:start] + opposite + female_version[end:] return male_version, female_version elif female_positions and not male_positions: # 只有女性词汇，创建男性版本 female_version = text male_version = text # 从后往前替换 for word, start, end in reversed(female_positions): opposite = self.gender_processor.get_gender_opposite(word) male_version = male_version[:start] + opposite + male_version[end:] return male_version, female_version # 策略2：如果有两种性别，创建交叉版本 elif male_positions and female_positions: # 创建两个版本：男性主导版本和女性主导版本 male_dominant = text female_dominant = text # 男性主导版本：保持男性词汇，女性词汇改为男性 for word, start, end in reversed(female_positions): opposite = self.gender_processor.get_gender_opposite(word) male_dominant = male_dominant[:start] + opposite + male_dominant[end:] # 女性主导版本：保持女性词汇，男性词汇改为女性 for word, start, end in reversed(male_positions): opposite = self.gender_processor.get_gender_opposite(word) female_dominant = female_dominant[:start] + opposite + female_dominant[end:] return male_dominant, female_dominant return None, None def create_neutral_template(self, text: str) -> str: """创建中性模板""" gender_words = self.gender_processor.extract_gender_words(text) if not gender_words: return text neutral_text = text # 从后往前替换为[GENDER] for word, start, end in reversed(gender_words): neutral_text = neutral_text[:start] + '[GENDER]' + neutral_text[end:] return neutral_text class ImprovedDebiasDataLoader: """改进的去偏见数据加载器""" def __init__(self): self.stereotype_converter = SmartStereotypeConverter() self.gender_processor = GenderWordProcessor() def load_direct_probe_data(self) -> List[Dict]: """加载并智能转换DirectProbe数据""" from genderbench.probes.direct.direct_probe import DirectProbe probe = DirectProbe() items = probe._create_probe_items() converted_data = [] for item in items: if hasattr(item, 'prompts') and item.prompts: # 提取原始stereotype prompt_text = item.prompts[0].text match = re.search(r'"([^"]+)"', prompt_text) if match: stereotype = match.group(1) # 智能转换 male_version, female_version = self.stereotype_converter.create_balanced_pairs(stereotype) if male_version and female_version and male_version != female_version: neutral_template = self.stereotype_converter.create_neutral_template(stereotype) converted_data.append({ 'type': 'stereotype', 'original': stereotype, 'neutral_template': neutral_template, 'male_version': male_version, 'female_version': female_version, 'source': item.metadata.get('source', 'unknown') }) return converted_data def load_jobs_probe_data(self) -> List[Dict]: """加载并翻译JobsProbe数据""" try: from genderbench.probes.jobs_lum.jobs_lum_probe import JobsLumProbe probe = JobsLumProbe() items = probe._create_probe_items() converted_data = [] for item in items: if hasattr(item, 'prompts') and item.prompts: prompt_text = item.prompts[0].text match = re.search(r'works as a/an (.+?)\. Please', prompt_text) if match: job_en = match.group(1) job_cn = self.gender_processor.translate_job(job_en) converted_data.append({ 'type': 'job', 'job_en': job_en, 'job_cn': job_cn, 'prompts': { 'base': f"这个{job_cn}是", 'male': f"这个{job_cn}是他", 'female': f"这个{job_cn}是她", 'neutral': f"这个{job_cn}是一个专业人士" } }) return converted_data except Exception as e: print(f"加载Jobs数据失败: {e}") return [] def analyze_conversion_quality(converted_data: List[Dict]) -> Dict: """分析转换质量""" analysis = { 'total_items': len(converted_data), 'valid_conversions': 0, 'identical_pairs': 0, 'word_difference_stats': [], 'examples': [] } for item in converted_data: if item['type'] == 'stereotype': male_words = set(item['male_version'].lower().split()) female_words = set(item['female_version'].lower().split()) # 计算词汇差异 diff_count = len(male_words.symmetric_difference(female_words)) analysis['word_difference_stats'].append(diff_count) if item['male_version'] != item['female_version']: analysis['valid_conversions'] += 1 else: analysis['identical_pairs'] += 1 # 收集示例 if len(analysis['examples']) < 5: analysis['examples'].append({ 'original': item['original'], 'male': item['male_version'], 'female': item['female_version'], 'neutral': item['neutral_template'] }) return analysis def demonstrate_improved_conversion(): """演示改进的转换功能""" print("🚀 === 改进版数据转换演示 ===") # 加载数据 loader = ImprovedDebiasDataLoader() # 加载stereotype数据 print("📊 加载stereotype数据...") stereotype_data = loader.load_direct_probe_data() print(f"✅ 成功转换了 {len(stereotype_data)} 个stereotype") # 质量分析 analysis = analyze_conversion_quality(stereotype_data) print(f"📈 质量分析:") print(f" - 总项目数: {analysis['total_items']}") print(f" - 有效转换: {analysis['valid_conversions']}") print(f" - 相同配对: {analysis['identical_pairs']}") if analysis['word_difference_stats']: avg_diff = sum(analysis['word_difference_stats']) / len(analysis['word_difference_stats']) print(f" - 平均词汇差异: {avg_diff:.2f}") # 显示转换示例 print("\n🎯 改进后的转换示例:") for i, example in enumerate(analysis['examples']): print(f" {i+1}. 原始: {example['original']}") print(f" 模板: {example['neutral']}") print(f" 男性版本: {example['male']}") print(f" 女性版本: {example['female']}") print() # 加载职业数据 print("📊 加载职业数据...") jobs_data = loader.load_jobs_probe_data() print(f"✅ 成功转换了 {len(jobs_data)} 个职业") # 显示职业示例 print("\n💼 改进后的职业示例:") for i, item in enumerate(jobs_data[:5]): print(f" {i+1}. 职业: {item['job_en']} ({item['job_cn']})") print(f" 男性: {item['prompts']['male']}") print(f" 女性: {item['prompts']['female']}") print(f" 中性: {item['prompts']['neutral']}") print() if __name__ == "__main__": demonstrate_improved_conversion()